An embodiment relates generally to object detection systems.
Human detection is an important operation of the object detection applications that may be used with automotive safety, smart surveillance systems, and factory work environments. From a scientific point of view, human detection incorporates most of the difficulties characterizing object detection in general, namely viewpoint, scale and articulation problems. Several approaches have been put forward that utilize machine learning to train a computer to recognize a class of objects. Many methods are part-based methods that decompose the object into parts; however, such techniques are deficient with respect to performance (e.g., occlusion, pose variation) and scalability (e.g., number of parts, model complexity). It is widely acknowledged that a method's detection performance largely depends on the richness and quality of the features used, and the ability to combine diverse feature families. As a result, deficiencies such as performance and scalability continue to be issues with known techniques.